International Journal of Molecular Sciences

Article Prolactin-Releasing Peptide Differentially Regulates Gene Transcriptomic Profiles in Mouse Bone Marrow-Derived Macrophages

Yulong Sun 1,2,* , Zhuo Zuo 1,2 and Yuanyuan Kuang 1,2

1 School of Life Sciences, Northwestern Polytechnical University, Xi’an 710072, China; [email protected] (Z.Z.); [email protected] (Y.K.) 2 Key Laboratory for Space Biosciences & Biotechnology, Institute of Special Environmental Biophysics, School of Life Sciences, Northwestern Polytechnical University, Xi’an 710072, China * Correspondence: [email protected]; Tel.: +86-29-8846-0332

Abstract: Prolactin-releasing Peptide (PrRP) is a neuropeptide whose receptor is GPR10. Recently, the regulatory role of PrRP in the neuroendocrine field has attracted increasing attention. However, the influence of PrRP on macrophages, the critical housekeeper in the neuroendocrine field, has not yet been fully elucidated. Here, we investigated the effect of PrRP on the transcriptome of mouse bone marrow-derived macrophages (BMDMs) with RNA sequencing, bioinformatics, and molecular simulation. BMDMs were exposed to PrRP (18 h) and were subjected to RNA sequencing. Differentially expressed genes (DEGs) were acquired, followed by GO, KEGG, and PPI analysis. Eight qPCR-validated DEGs were chosen as hub genes. Next, the three-dimensional structures



 of the proteins encoded by these hub genes were modeled by Rosetta and Modeller, followed by molecular dynamics simulation by the Gromacs program. Finally, the binding modes between PrRP Citation: Sun, Y.; Zuo, Z.; Kuang, Y. and hub proteins were investigated with the Rosetta program. PrRP showed no noticeable effect on Prolactin-Releasing Peptide the morphology of macrophages. A total of 410 DEGs were acquired, and PrRP regulated multiple Differentially Regulates Gene BMDM-mediated functional pathways. Besides, the possible docking modes between PrRP and hub Transcriptomic Profiles in Mouse proteins were investigated. Moreover, GPR10 was expressed on the cell membrane of BMDMs, which Bone Marrow-Derived Macrophages. Int. J. Mol. Sci. 2021, 22, 4456. increased after PrRP exposure. Collectively, PrRP significantly changed the transcriptome profile https://doi.org/10.3390/ijms22094456 of BMDMs, implying that PrRP may be involved in various physiological activities mastered by macrophages. Academic Editor: Sarath Chandra Janga Keywords: prolactin-releasing peptide; GPR10; bone marrow-derived macrophage; RNA sequencing; bioinformatics; transcriptomic profiles Received: 2 March 2021 Accepted: 20 April 2021 Published: 24 April 2021 1. Introduction Publisher’s Note: MDPI stays neutral Prolactin-releasing peptide (PrRP) is a neuropeptide originally extracted from the with regard to jurisdictional claims in bovine hypothalamus [1,2]. PrRP has two biologically active isoforms, PrRP20 and PrRP31, published maps and institutional affil- which share identical C-terminal Arg-Phe-amide sequences [3,4]. GPR10 (G-protein cou- iations. pled receptor 10, GPCR 10) is considered as the endogenous receptor for PrRP [2,5]. Both isoforms PrRP20 and PrRP31 bind to GPR10 with high affinity [6]. PrRP belongs to a family named RF-amide, which includes neuropeptide FF (NPFF), gonadotropin-inhibitory hormone (GnIH), kisspeptin, and pyroglutamylated RFamide (QRFP) [7]. Copyright: © 2021 by the authors. The original biological function of PrRP is to promote the release of prolactin from cul- Licensee MDPI, Basel, Switzerland. tured pituitary cells [2]. Then, follow-up research results show that PrRP has an important This article is an open access article role in the neuroendocrine system, including reducing food intake in fasting and free- distributed under the terms and eating rat models [8], regulating energy homeostasis and increasing energy expenditure [9], conditions of the Creative Commons mediating the anorexia effect of nerves [10], protecting neuron on several rodent neurode- Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ generative disorder models [11], and participating in stress response [12]. However, the 4.0/). effect of PrRP on macrophages has not been reported yet.

Int. J. Mol. Sci. 2021, 22, 4456. https://doi.org/10.3390/ijms22094456 https://www.mdpi.com/journal/ijms Int.Int. J.J. Mol.Mol. Sci. 2021,, 22,, 4456x FOR PEER REVIEW 22 of of 4651

The regulatory effect of PrRPPrRP on leukocytes has been investigated. Romeroa and col- leagues findfind that PrRPPrRP mRNA isis expressed inin leukocytesleukocytes fromfrom thethe headhead kidneykidney andand bloodblood of Salmon salar.. Moreover, syntheticsynthetic PrRPPrRP promotespromotes thethe expressionexpression ofof pro-inflammatorypro-inflammatory cytokines (IL-12,(IL-12, IL-6,IL-6, IL-8,IL-8, and and IL-1 IL-1ββ),), suggesting suggesting that that PrRP PrRP may may be be a local a local neurotransmit- neurotrans- termitter of innate of innate immune immune processes processes in leukocytesin leukocytes from fromSalmon Salmon salar salar[13 [13].]. However, However, the the target tar- genesget genes by whichby which PrRP PrRP affects affects the leukocytesthe leukocytes have have not beennot been identified identified yet. yet. RNA-seq (RNA-sequencing)(RNA-sequencing) isis a a useful useful method method to to explore explore the the effect effect of theof the peptide peptide on theon genethe gene expression expression profile profile of macrophages of macropha fromges the from transcriptome the transcriptome level [14 –level16]. Hence, [14–16]. it wouldHence, beit would useful be to investigateuseful to investigate the PrRP-induced the PrRP-induced gene expression gene expression profile of profile macrophages of mac- byrophages using RNA-seq. by using RNA-seq. In the present In the study, present the stud effecty, of the PrRP effect on of the PrRP transcriptome on the transcriptome of BMDM wasof BMDM studied was by studied the following by the procedures:following procedures: (a)(a) PrRP31-triggeredPrRP31-triggered differentially expressed genes (DEGs) were acquired from murinemurine bonebone marrow-derived marrow-derived macrophages (BMDMs). Cells treated with 1 nM PrRP31 (18 h) werewere detected by RNA-seq. A A total of 410410 DEGsDEGs werewere obtained.obtained. In addition,addition, thethe influenceinfluence of of PrRP PrRP on on the the morphology morphology of of cells cells was was observed observed with with optical optical microscopy. microscopy. (b)(b) DEGsDEGs were were analyzed by a series of bioinf bioinformaticsormatics approaches, includingincluding GOGO analysis,analysis, functionalfunctional enrichment, enrichment, and protein–protein interaction (PPI)(PPI) studies.studies. Next,Next, eighteight hubhub genesgenes were were finally finally acquired for subsequent study. (c) The three-dimensional structures of hub proteins were studied. By using homology- (c) The three-dimensional structures of hub proteins were studied. By using homology- modeling, the structure of proteins encoded by hub genes (hub proteins) was built. modeling, the structure of proteins encoded by hub genes (hub proteins) was built. Subsequently, molecular dynamics simulations (at least 300 ns) were performed to Subsequently, molecular dynamics simulations (at least 300 ns) were performed to capture the trajectory of hub proteins. Finally, molecular dynamics-optimized protein capture the trajectory of hub proteins. Finally, molecular dynamics-optimized structures of hub proteins were obtained. protein structures of hub proteins were obtained. (d) The docking models of PrRP and hub proteins were predicted with the peptide-protein (d) The docking models of PrRP and hub proteins were predicted with the peptide- docking module of the Rosetta program. protein docking module of the Rosetta program. By studying the influence of PrRP on the gene expression of BMDMs at the tran- By studying the influence of PrRP on the gene expression of BMDMs at the transcrip- scriptome level, we provide clues for exploring the gene expression network of PrRP on tome level, we provide clues for exploring the gene expression network of PrRP on mac- macrophages, which will be helpful to investigate the immune-regulating function of PrRP inrophages, the future which (Figure will1 ).be helpful to investigate the immune-regulating function of PrRP in the future (Figure 1).

Figure 1. Schematic diagram of this study.

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2. Results

Int. J. Mol. Sci. 2021, 22, 4456 2.1. PrRP Demonstrated No Significant Effect on the Shape of BMDMs 3 of 46 As displayed in Figure 2A, the cell morphology of BMDMs was detected by electron microscopy. In addition, the purity of BMDMs was evaluated using flow cytometry with anti-CD11b and anti-F4/80 (Figure 2B). The results of flow cytometry showed that the dou- 2. Results ble-positive rate of the BMDMs control group (no antibody was added) was 0.14% (the 2.1. PrRP Demonstrated No Significant Effect on the Shape of BMDMs left side of Figure 2B), whereas the double-positive rate of BMDMs (incubated with anti- F4/80As and displayed anti-CD11b) in Figure 96.7%2A, (right the cell side morphology of Figure 2B). of BMDMs Hence, these was detected data hinted by electron that the microscopy.purity of BMDMs In addition, was acceptable. the purity of BMDMs was evaluated using flow cytometry with anti-CD11bNext, the and morphological anti-F4/80 (Figure characters2B). Theof BMDMs results before of flow and cytometry after PrRP showed exposure that were the double-positive rate of the BMDMs control group (no antibody was added) was 0.14% examined with an optical microscope. As shown in Figure 2C, BMDMs were oval before (the left side of Figure2B), whereas the double-positive rate of BMDMs (incubated with PrRP (1 nM) exposure, and the shape of BMDMs did not change remarkably after PrRP anti-F4/80 and anti-CD11b) 96.7% (right side of Figure2B). Hence, these data hinted that exposure for 18 h. Overall, PrRP demonstrated no significant effect on the shape of the purity of BMDMs was acceptable. BMDMs.

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(C) Figure 2. The effect of PrRP31 exposure on BMDMs. (A) The cell morphology of BMDMs was examined with electron Figure 2. The effect of PrRP31 exposure on BMDMs. (A) The cell morphology of BMDMs was examined with electron microscopy. Scale bar, 50 μm. (B) BMDMs were analyzed by the flow cytometer with FITC-conjugated anti-CD11b and µ B microscopy.PE-conjugated Scale anti-F4/80. bar, 50 Form. the ( ) experime BMDMsntal were group, analyzed the cells by the were flow incubated cytometer with with FITC-conjugated FITC-conjugated anti-CD11b anti-CD11b and and PE- PE-conjugatedconjugated anti-F4/80, anti-F4/80. and the For do theuble-positive experimental rate group, was 96.7%. the cells (C) were BMDMs incubated were incubated with FITC-conjugated with vehicle (phosphate anti-CD11b buffer and PE-conjugatedsaline) or PrRPanti-F4/80, (1 nM) for 0 and h or the 18 double-positiveh, and the morphology rate was of 96.7%. cells was (C) exam BMDMsined were under incubated a microscope. with vehicle Scale bar, (phosphate 100 μm. buffer saline) or PrRP (1 nM) for 0 h or 18 h, and the morphology of cells was examined under a microscope. Scale bar, 100 µm. 2.2. Identification of DEGs To explore the effect of PrRP on the transcriptome of BMDMs, cell samples were de- Next, the morphological characters of BMDMs before and after PrRP exposure were tected by RNA-seq sequencing (Figure 3A–D, Table S1, and Supplementary File 2). The examined with an optical microscope. As shown in Figure2C, BMDMs were oval before quality control test results showed that our RNA-seq was qualified (Supplementary File PrRP (1 nM) exposure, and the shape of BMDMs did not change remarkably after PrRP exposureS2 Figure for S1A 18 and h. Overall, S1B). A PrRP total demonstratedof 410 DEGs were no significant obtained, effect of which on the 371 shape genes of were BMDMs. up- regulated, and 39 genes were down-regulated (p-value < 0.05 and |log2(fc)| > 1) (Figures 2.2.3C, Supplementary Identification of DEGs Files 1 and 3). A heatmap and volcano map displayed the distribution of genesTo explore in each the group effect (Figure of PrRP 3B,D). on theAs transcriptomedemonstrated in of Tables BMDMs, 1–3, cell PrRP samples stimulated were detected(371 up-regulated by RNA-seq genes) sequencing more genes (Figure than 3suppressingA–D, Table genes S1, and (39 Supplementary down-regulated File genes) 2). Theon the quality transcriptional control test level results of BMDMs. showed thatIn addition, our RNA-seq DEGs wasstimulated qualified by (SupplementaryPrRP were com- Fileposed S2 of Figure the following S1A,B). A types total ofof 410genes: DEGs protein_coding were obtained, genes of which (94.43%), 371 geneslincRNA were genes up- regulated,(1.03%), antisense and 39 genes genes were (0.66%), down-regulated and other genes (p-value (3.88%) < 0.05 (Figure and |log2(fc)| 3A). > 1) (Figure3C , Supplementary Files 1 and 3). A heatmap and volcano map displayed the distribution of genes in each group (Figure3B,D). As demonstrated in Tables1–3, PrRP stimulated (371 up-regulated genes) more genes than suppressing genes (39 down-regulated genes) on the transcriptional level of BMDMs. In addition, DEGs stimulated by PrRP were composed of the following types of genes: protein_coding genes (94.43%), lincRNA genes (1.03%), antisense genes (0.66%), and other genes (3.88%) (Figure3A). Int. J. Mol. Sci. 2021, 22, 4456 4 of 46 Int. J. Mol. Sci. 2021, 22, x FOR PEER REVIEW 4 of 51

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FigureFigure 3. 3. BasicBasic information information of ofthe the RNA-seq RNA-seq results. results. (A ()A Proportion) Proportion plot plot of ofthe the differentiated differentiated ex- expressed pressedgenes (genesp-value (p-value < 0.05 < and 0.05 |log2(fc)|and |log2(fc)| > 1) > (DEGs).1) (DEGs). (B ()B Volcano) Volcano plot plot (red (red dots: dots: up-regulatedup-regulated genes; genes; green dots: down-regulated genes). (C) Number of DEGs (p-value < 0.05 and |log2(fc)| > 1). green dots: down-regulated genes). (C) Number of DEGs (p-value < 0.05 and |log2(fc)| > 1). (D) (D) Heat map of the 24 up-regulated DEGs, 24 down-regulated DEGs, and 8 hub genes, as the rankingHeat map was of based the 24onup-regulated the absolute value DEGs, of 24the down-regulated fold of change in DEGs, descending and 8 order. hub genes, The red as indi- the ranking catedwas basedthe up-regulated on the absolute genes and value blue of represented the fold of changedown-regulated in descending genes. order. The red indicated the up-regulated genes and blue represented down-regulated genes. Table 1. Details of DEGs-protein_coding (up-regulated and down-regulated 24 proteincod- ingotein_coding genes).

Gene_Symbol log2FoldChange Padj Gene_Chrosome Change Entpd4b –3.707639059 2.19 × 10−2 14 Down Hspb7 –3.272825898 1.68 × 10−2 4 Down Dclk2 –2.383273176 4.25 × 10−2 3 Down Slc7a15 –2.101674471 2.79 × 10−2 12 Down Int. J. Mol. Sci. 2021, 22, 4456 6 of 46

Table 1. Details of DEGs-protein_coding (up-regulated and down-regulated 24 proteincodin- gotein_coding genes).

Gene_Symbol log2FoldChange Padj Gene_Chrosome Change Entpd4b −3.707639059 2.19 × 10−2 14 Down Hspb7 −3.272825898 1.68 × 10−2 4 Down Dclk2 −2.383273176 4.25 × 10−2 3 Down Slc7a15 −2.101674471 2.79 × 10−2 12 Down Nanos1 −1.666406569 6.68 × 10−2 19 Down Dach1 −1.613454215 1.72 × 10−3 14 Down Srpk3 −1.591562463 6.24 × 10−3 X Down Slc6a4 −1.584727023 3.21 × 10−2 11 Down Nptx1 −1.450738194 3.30 × 10−6 11 Down Smpd3 −1.391289481 7.65 × 10−4 8 Down Pgm5 −1.31912871 4.92 × 10−2 19 Down Hmga2 −1.300711982 2.19 × 10−14 10 Down Fam78b −1.285355042 1.93 × 10−3 1 Down Cmbl −1.256341752 2.78 × 10−2 15 Down Chst3 −1.22498333 1.07 × 10−3 10 Down Slco2b1 −1.20549131 1.27 × 10−4 7 Down Klhl3 −1.173243947 1.22 × 10−2 13 Down Slc24a3 −1.163460987 1.40 × 10−4 2 Down Prokr1 −1.100130971 9.67 × 10−4 6 Down Efr3b −1.063158734 5.39 × 10−13 12 Down Sox4 −1.052753293 2.51 × 10−2 13 Down Fabp7 −1.049413652 1.71 × 10−2 10 Down Etl4 −1.039530137 1.92 × 10−3 2 Down Angptl2 −1.033436675 1.59 × 10−23 2 Down Saa3 6.725187234 1.04 × 10−272 7 Up Cfb 6.339511013 1.73 × 10−5 17 Up Lad1 6.049625834 8.45 × 10−5 1 Up Iigp1 5.288407326 5.88 × 10−35 18 Up Cacng8 5.269289134 1.18 × 10−4 7 Up Ly6i 5.021584581 5.25 × 10−41 15 Up Serpina3g 4.932160399 1.43 × 10−11 12 Up Cdkn2a 4.865779496 9.13 × 10−3 4 Up Ptx3 4.863346866 8.39 × 10−3 3 Up Acod1 4.818905659 2.31 × 10−292 14 Up Adgre4 4.767373764 1.20 × 10−94 17 Up Il1b 4.68955843 1.37 × 10−46 2 Up Ly6a 4.571707887 8.85 × 10−71 15 Up Lpar1 4.533771177 4.00 × 10−6 4 Up Fpr1 4.518260903 7.01 × 10−59 17 Up Cxcl10 4.51118115 8.99 × 10−41 5 Up Ccl5 4.470370636 2.30 × 10−128 11 Up Gbp4 4.398911115 4.55 × 10−11 5 Up Marco 4.320725477 6.79 × 10−122 1 Up Fpr2 4.318699393 5.20 × 10−73 17 Up Ly6c2 4.262622986 2.41 × 10−27 15 Up Serpina3f 4.249141119 3.35 × 10−5 12 Up Ppm1n 4.157670609 4.40 × 10−16 7 Up Apol9b 4.156227055 1.44 × 10−2 15 Up Int. J. Mol. Sci. 2021, 22, 4456 7 of 46

Table 2. Details of DEGs-antisense RNA.

Gene_Symbol log2FoldChange Padj Gene_Chrosome Change Gm45820 −1.686832907 2.71 × 10−2 8 Down Gm28800 −1.563748269 2.10 × 10−2 1 Down Dnmt3aos −1.056605119 2.92 × 10−5 12 Down Gm13822 4.654103949 2.29 × 10−2 5 Up AC113595.1 1.919370036 7.44 × 10−4 15 Up Gm11772 1.832830691 1.76 × 10−4 11 Up

Table 3. Details of DEGs-lincRNA.

Gene_Symbol log2FoldChange Padj Gene_Chrosome Change AW112010 2.84032013 3.87 × 10−31 19 Up Gm34643 2.150264718 2.24 × 10−5 14 Up Gm36161 1.365143899 6.92 × 10−26 13 Up Gm17705 1.333355523 7.90 × 10−3 17 Up Gm14221 −1.62639326 2.98 × 10−4 2 Down Gm37168 −1.332031628 4.17 × 10−5 1 Down Gm16907 −1.027914414 7.90 × 10−3 13 Down

2.3. Functional and Pathway Enrichment Analysis of DEGs To explore the biological meanings of DEGs, several tools were used to analyze the functions of DEGs, such as Metascape, KEGG, and PANTHER. The KEGG online tool was used to explore the enrichment pathways of DEGs. As shown in Table S2, DEGs were involved in several biological pathways: autoimmune thy- roid disease (mmu05320; gene count: 110), Epstein–Barr virus infection (mmu05169; gene count: 24), proteasome (mmu03050; gene count: 127), and allograft rejection (mmu05330; gene count: 46) (Table S2). The Metascape online website was employed to study the functional enrichment of DEGs. All DEGs were subjected to Metascape to acquire the enrichment pathways. The down-regulated DEGs activated several pathways such as the glycosaminoglycan biosynthetic process, brain development, sodium ion transport, and central nervous sys- tem neuron differentiation (Figure4A,D,G). Meanwhile, enriched pathways stimulated by up-regulated DEGs mainly included the regulation of defense response, response to interferon-beta, response to virus, response to interferon-gamma, inflammatory response, Herpes simplex infection (Figure4B,E,H). Besides, all DEGs activated a series of path- ways, including regulation of defense response, response to interferon-beta, response to virus, response to interferon-gamma, inflammatory response, and Herpes simplex infec- tion (Figure4C,F,I) . Finally, these pathways were clustered, followed by connecting with various network diagrams (Figure4G–I). The online tool PANTHER was also employed to investigate the enriched processes of DEGs. All DEGs were classified into the following categories: molecular function (MF), biological process (BP), and cellular compartment (CC). For the biological processes, the biological processes caused by down-regulated DEGs were: cellular process (GO:0009987, 25.7%), metabolic process (GO:0008152, 22.9%), and biological regulation (GO:0065007, 17.1%) (Figure5A). Meanwhile, up-DEG-caused bi- ological processes were mainly composed of the following processes: cellular process (GO:0009987, 19.5%), response to a stimulus (GO:0050896, 13.5%), and biological regulation (GO:0065007, 13.5%) (Figure5B). Moreover, all DEGs affected diverse pathways such as cellular process (GO:0009987), biological regulation (GO:0065007), response to stimulus (GO:0050896), and metabolic process (GO:0008152) (Figure5C). Int. J. Mol. Sci. 2021, 22, x FOR PEER REVIEW 8 of 51

As displayed in Figure 5E, down-regulated activated the following processes: cata- lytic activity (GO:0003824, 50%), transporter activity (GO:0005215, 33.3%), and binding (GO:0005488, 8.3%) (Figure 5G). Besides, up-regulated DEGs were involved in the follow- ing activities: binding (GO:0005488, 42.8%), catalytic activity (GO:0003824, 30%), and mo- Int. J. Mol. Sci. 2021, 22, 4456 lecular function regulator (GO:0098772, 11.4%) (Figure 5H). Besides, all DEGs provoked8 of 46 a number of pathways, including binding (GO:0005488), catalytic activity (GO:0003824), and molecular function regulator (GO:0098772) (Figure 5I).

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FigureFigure 4. The 4. The enrichment enrichment analysis analysis results results by by using using Metascape.Metascape. ( A––CC)) Bar Bar plots plots of of enriched enriched terms terms for fordown-regulated down-regulated DEGs DEGs (A), up-regulated DEGs (B), and all DEGs (C) (colored by p-values) were presented. (D–F) Network of enriched terms A B C D F ( ),(colored up-regulated by cluster DEGs ID), ( where), and nodes all DEGs that shared ( ) (colored the same by clus p-values)ter ID were were typically presented. close ( to– each) Network other. The of networks enriched for terms (colored by cluster ID), where nodes that shared the same cluster ID were typically close to each other. The networks for down-regulated DEGs (D), up-regulated DEGs (E), and all DEGs (F) were shown. (G–I) Network of enriched terms (colored by p-value), where terms containing more genes tended to have a more significant p-value. Network plots for down-regulated DEGs (G), up-regulated DEGs (H), and all DEGs (I) were demonstrated. Int. J. Mol. Sci. 2021, 22, x FOR PEER REVIEW 11 of 51

Int. J. Mol. Sci.down-regulated2021, 22, 4456 DEGs (D), up-regulated DEGs (E), and all DEGs (F) were shown. (G–I) Network of enriched terms11 (col- of 46 ored by p-value), where terms containing more genes tended to have a more significant p-value. Network plots for down- regulated DEGs (G), up-regulated DEGs (H), and all DEGs (I) were demonstrated.

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(I) Figure 5. Functional enrichment analysis data of DEGs from PANTHER. The DEGs were analyzed with the PANTHER Figure 5. Functional enrichment analysis data of DEGs from PANTHER. The DEGs were analyzed with the PANTHER GO classification. (A–C) Biological process (BP) of down-regulated DEGs (A), up-regulated DEGs (B), and all DEGs (C). GO classification. (A–C) Biological process (BP) of down-regulated DEGs (A), up-regulated DEGs (B), and all DEGs (C). (D–F) Cellular component (CC) of down-regulated DEGs (D), up-regulated DEGs (E), and all DEGs (F). (G–I) Molecular function (MF) of down-regulated DEGs (G), up-regulated DEGs (H), and all DEGs (I). Int. J. Mol. Sci. 2021, 22, 4456 14 of 46

As for the cellular compartment pathway, down-regulated DEGs activated the fol- lowing processes: cell part (GO:0044464, 23.9%), cell (GO:0005623, 23.9%), and organelle (GO:0043226, 13%) (Figure5D). Meanwhile, the main pathways activated by up-regulated DEGs were cell part (GO:0044464, 19.4%), cell (GO:0005623, 19.4%), and membrane (GO:0016020, 10.8%) (Figure5E). In addition, all DEGs were involved in the following pathways: cellular anatomical entity (GO:0110165), intracellular (GO:0005622), and protein- containing complex (GO:0032991) (Figure5F). As displayed in Figure5E, down-regulated activated the following processes: cat- alytic activity (GO:0003824, 50%), transporter activity (GO:0005215, 33.3%), and binding (GO:0005488, 8.3%) (Figure5G). Besides, up-regulated DEGs were involved in the fol- lowing activities: binding (GO:0005488, 42.8%), catalytic activity (GO:0003824, 30%), and molecular function regulator (GO:0098772, 11.4%) (Figure5H). Besides, all DEGs provoked a number of pathways, including binding (GO:0005488), catalytic activity (GO:0003824), and molecular function regulator (GO:0098772) (Figure5I).

2.4. Protein–Protein Interaction (PPI) Network Analysis To explore the DEGs-induced protein–protein interactions, DEGs were analyzed with the online tool STRING database, followed by visualizing with Cytoscape. By using the Cytoscape’s plug-in cyto-Hubba, a total of eight hub genes (IFIT1, OASL2, IRF7, IFIT3, IFIT2, USP18, IFI44, and RTP4) were acquired with the highest scores. Besides, the Cytoscape plug-in ClueGO was used to study DEGs-induced functional processes. Functional enrichment pathways analysis showed that the down-regulated DEGs stim- ulated the glycosaminoglycan biosynthetic process (Figure6A). Meanwhile, up-regulated DEGs were involved in the activation of the following pathways: C-type lectin receptor signaling pathways, protein digestion and absorption, proteoglycans in cancer, arrhyth- mogenic right ventricular cardiomyopathy (ARVC), primary immunodeficiency, adipocy- tokine signaling pathway, Rap1 signaling pathway (Figure6B); relaxin signaling pathway and nicotinate and nicotinamide metabolism (Figure6C); Malaria, JAK-STAT signaling pathway, human papillomavirus infection, NF-kappa B signaling pathway (Figure6D); Staphylococcus aureus infection (Figure6E); TNF signaling pathway, cytokine-cytokine receptor interaction, and pertussis (Figure6F); Influenza A, Herpes simplex virus 1 in- fection, and bladder cancer (Figure6G). Moreover, a series of pathways were affected by all DEGs (Up- and down-regulated DEGs), including AGE-RAGE signaling pathway in diabetic compli-cations, malaria, Chagas disease (American trpanosomiasis), nicotinate and nicotinamide metabolism, amoebiasis, small cell lung cancer, African trypnosomiasis, prison diseases, bladder cancer, fluid shear stress and atherosclerosis, protein digestion and absorption, pertussis, glycosaminoglycan biosynthesis, proteoglycans in cancer, ar- rhythmogenic right ventricular cardiomyopathy (ARVC), platelet activation, necroptosis, osteoclast differenti-ation, apoptosis, complement and coagulation cascade, NOD-like receptor signaling pathway, NF-kappa B signaling pathway, C-type lection receptor sig- naling pathway, p53 signaling pathway, transcriptional misregulation in cancer, adipocy- tokine signaling pathway, Rap1 signaling pathway, relaxin signaling pathway, and primary immunode-ficiency (Figure6H); TNF signaling pathway, cytokine-cytokine receptor in- teraction, JAK-STAT signaling pathway, cytosolic DNA-sensing pathway, and pyrimidine metabolism (Figure6I); Le-gionellosis and human papillomavirus infection (Figure6J); Type I diabetes mellitus, Herpes simplex virus 1 infection, Epstein–Barr virus infection, autoimmune thyroid disease, and Leishmaniasis (Figure6K). Int. J. Mol. Sci. 2021, 22, x FOR PEER REVIEW 14 of 51

(D–F) Cellular component (CC) of down-regulated DEGs (D), up-regulated DEGs (E), and all DEGs (F). (G–I) Molecular function (MF) of down-regulated DEGs (G), up-regulated DEGs (H), and all DEGs (I).

2.4. Protein–Protein Interaction (PPI) Network Analysis To explore the DEGs-induced protein–protein interactions, DEGs were analyzed with the online tool STRING database, followed by visualizing with Cytoscape. By using the Cytoscape’s plug-in cyto-Hubba, a total of eight hub genes (IFIT1, OASL2, IRF7, IFIT3, IFIT2, USP18, IFI44, and RTP4) were acquired with the highest scores. Besides, the Cyto- scape plug-in ClueGO was used to study DEGs-induced functional processes. Functional enrichment pathways analysis showed that the down-regulated DEGs stimulated the glycosaminoglycan biosynthetic process (Figure 6A). Meanwhile, up-reg- ulated DEGs were involved in the activation of the following pathways: C-type lectin re- ceptor signaling pathways, protein digestion and absorption, proteoglycans in cancer, ar- rhythmogenic right ventricular cardiomyopathy (ARVC), primary immunodeficiency, adipocytokine signaling pathway, Rap1 signaling pathway (Figure 6B); relaxin signaling pathway and nicotinate and nicotinamide metabolism (Figure 6C); Malaria, JAK-STAT signaling pathway, human papillomavirus infection, NF-kappa B signaling pathway (Fig- ure 6D); Staphylococcus aureus infection (Figure 6E); TNF signaling pathway, cytokine- cytokine receptor interaction, and pertussis (Figure 6F); Influenza A, Herpes simplex virus 1 infection, and bladder cancer (Figure 6G). Moreover, a series of pathways were affected by all DEGs (Up- and down-regulated DEGs), including AGE-RAGE signaling pathway in diabetic compli-cations, malaria, Chagas disease (American trpanosomiasis), nicotinate and nicotinamide metabolism, amoebiasis, small cell lung cancer, African trypnosomiasis, prison diseases, bladder cancer, fluid shear stress and atherosclerosis, protein digestion and absorption, pertussis, glycosaminoglycan biosynthesis, proteoglycans in cancer, ar- rhythmogenic right ventricular cardiomyopathy (ARVC), platelet activation, necroptosis, osteoclast differenti-ation, apoptosis, complement and coagulation cascade, NOD-like re- ceptor signaling pathway, NF-kappa B signaling pathway, C-type lection receptor signal- ing pathway, p53 signaling pathway, transcriptional misregulation in cancer, adipocyto- kine signaling pathway, Rap1 signaling pathway, relaxin signaling pathway, and primary immunode-ficiency (Figure 6H); TNF signaling pathway, cytokine-cytokine receptor in- teraction, JAK-STAT signaling pathway, cytosolic DNA-sensing pathway, and pyrimi- dine metabolism (Figure 6I); Le-gionellosis and human papillomavirus infection (Figure 6J); Type I diabetes mellitus, Herpes simplex virus 1 infection, Epstein–Barr virus infec- tion, autoimmune thyroid disease, and Leishmaniasis (Figure 6K). Int. J. Mol. Sci. 2021, 22, 4456 In addition, the GO analysis and detailed information of eight hub genes were listed 15 of 46 in Tables 4 and 5. Besides, the PPI interaction between DEGs was analyzed with the Cy- toscape Plug-in Mcode. As demonstrated in Figure 7, four networks were activated by the up-regulated DEGs while no network was stimulated by the down-regulated DEGs.

Int. J. Mol. Sci. 2021, 22, x FOR PEER REVIEW 15 of 51

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FigureFigure 6. FunctionalFunctional enrichment enrichment analysis analysis for for DEGs DEGs using using ClueGO. ClueGO. Functional Functional enrichment enrichment for for down-regulated DEGs DEGs showedshowed that that glycosaminogly glycosaminoglycancan biosynthetic biosynthetic process process ( (AA)) was was affected. affected. Besides, Besides, the the up-regulated up-regulated DEGs DEGs activated activated various various pathways, including (B) C-type lectin receptor signaling pathways, protein digestion and absorption, proteoglycans in pathways, including (B) C-type lectin receptor signaling pathways, protein digestion and absorption, proteoglycans in cancer, arrhythmogenic right venticular cardiomyopathy (ARVC), primary immunodeficiency, adipocytokine signaling cancer, arrhythmogenic right venticular cardiomyopathy (ARVC), primary immunodeficiency, adipocytokine signaling pathway, Rap1 signaling pathway; (C) relaxin signaling pathway and nicotinate and nicotinamide metabolism; (D) Ma- laria,pathway, JAK-STAT Rap1 signaling signaling pathway; pathway, (C human) relaxin papillomavirus signaling pathway infection, and nicotinate NF-kappa and B signaling nicotinamide pathway; metabolism; (E) Staphylococcus (D) Malaria, aureusJAK-STAT infection; signaling (F) TNF pathway, signaling human pathway, papillomavirus cytokine-cytokine infection, NF-kappareceptor interaction, B signaling and pathway; pertussis; (E) Staphylococcus(G) Influenza A, aureus Her- pesinfection; simplex (F )virus TNF 1 signaling infection, pathway, and bladder cytokine-cytokine cancer. Moreover, receptor a series interaction, of pathways and pertussis; were affected (G) Influenza by all DEGs A, Herpes (include simplex Up- regulatedvirus 1 infection, and down-regulated and bladder cancer.DEGs), Moreover,including ( aH series) AGE-RAGE of pathways signaling were pathway affected byin diabetic all DEGs complications, (include Up-regulated malaria, Chagasand down-regulated disease (American DEGs), trypanosomiasis), including (H) AGE-RAGEnicotinate and signaling nicotinamide pathway metabolism, in diabetic amoebiasis, complications, small malaria,cell lung Chagascancer, Africandisease (Americantrypanosomiasis, trypanosomiasis), prison diseases, nicotinate bladder and cancer, nicotinamide fluid shear metabolism, stress and amoebiasis, atherosclerosis, small cell protein lung cancer,digestion African and absorption,trypanosomiasis, pertussis, prison glycosaminoglycan diseases, bladder biosynthesis, cancer, fluid proteoglycans shear stress and in atherosclerosis,cancer, arrhythmogenic protein digestion right ventricular and absorption, cardio- myopathy (ARVC), platelet activation, necroptosis, osteoclast differentiation, apoptosis, complement and coagulation cas- pertussis, glycosaminoglycan biosynthesis, proteoglycans in cancer, arrhythmogenic right ventricular cardiomyopathy cade, NOD-like receptor signaling pathway, NF-kappa B signaling pathway, C-type lection receptor signaling pathway, (ARVC), platelet activation, necroptosis, osteoclast differentiation, apoptosis, complement and coagulation cascade, NOD- p53 signaling pathway, transcriptional misregulation in cancer, adipocytokine signaling pathway, Rap1 signaling path- way,like receptorrelaxin signaling signaling pathway, pathway, and NF-kappa primary B signalingimmunodeficiency. pathway, C-type (I) TNF lection signaling receptor pathway, signaling cytokine-cytokine pathway, p53 signalingreceptor interaction,pathway, transcriptional JAK-STAT signaling misregulation pathway, in cytosolic cancer, DNA-sensing adipocytokine pathway, signaling and pathway, pyrimidine Rap1 metabolism. signaling pathway,(J) Legionellosis relaxin andsignaling human pathway, papillomavirus and primary infection. immunodeficiency. (K) Type I diabetes (I) TNF mellitus, signaling Herpes pathway, simplex cytokine-cytokine virus 1 infection, receptor Epstein–Barr interaction, virus infection,JAK-STAT autoimmune signaling pathway, thyroid cytosolic disease, and DNA-sensing Leishmaniasis. pathway, and pyrimidine metabolism. (J) Legionellosis and human papillomavirus infection. (K) Type I diabetes mellitus, Herpes simplex virus 1 infection, Epstein–Barr virus infection, autoimmune thyroid disease, and Leishmaniasis.

In addition, the GO analysis and detailed information of eight hub genes were listed in Tables4 and5. Besides, the PPI interaction between DEGs was analyzed with the Int. J. Mol. Sci. 2021, 22, 4456 19 of 46

Cytoscape Plug-in Mcode. As demonstrated in Figure7, four networks were activated by the up-regulated DEGs while no network was stimulated by the down-regulated DEGs.

Table 4. Hub genes of PrRP-treated BMDMs.

Expression Gene Name Species Location Changes Function Refs. Ensembl ID Gene Type Length (PrRP vs. Control) Ifit1 Activities: Inhibit the (Interferon Induced translational initiation and Protein with replication of virus. Mus musculus Chr 19 Tetratricopeptide Up-regulated Diseases: Hepatitis and Hepatitis [17] Protein coding (2638 bp) Repeats 1) C. (ENSEMBL: Pathways: innate immune system ENSG00000185745) and interferon gamma signaling. Activities: is involved in the innate immune response to viral Oasl2 infection. (2’-5’ oligoadenylate Mus musculus Chr 5 Diseases: microphthalmia with Up-regulated [18] synthetase-like 2) Protein coding (3136 bp) limb anomalies and tick-Borne (ENSMUSG00000029561) encephalitis. Pathways: innate immune system and interferon gamma signaling. Activities: regulates the transcriptional activation of virus-inducible cellular genes Irf7 such as interferon beta chain (Interferon Regulatory Mus musculus Chr 7 genes. Up-regulated [19] Factor 7) Protein coding (1876 bp) Diseases: influenza and (ENSG00000185507) immunodeficiency 39. Pathways: activated TLR4 signaling and apoptosis modulation and signaling. Activities: inhibits cellular events, including viral processes, Ifit3 signaling, proliferation, cell (Interferon Induced migration, and viral replication. Protein with Mus musculus Chr 19 Up-regulated Diseases: systemic lupus [17] Tetratricopeptide Protein coding (1998 bp) erythematosus and lupus Repeats 3) erythematosus. (ENSG00000119917) Pathways: innate immune system and interferon gamma signaling. Ifit2 Activities: inhibits the expression (Interferon Induced of viral mRNAs. Protein with Mus musculus Chr 19 Diseases: microphthalmia with Up-regulated [20] Tetratricopeptide Protein coding (3949 bp) limb anomalies. Repeats 2) Pathways: innate immune system (ENSG00000119922) and interferon gamma signaling. Activities: inhibits interferon responses. Usp18 Diseases: torch syndrome and (Ubiquitin Specific Mus musculus Chr 6 pseudo-torch syndrome 2. Up-regulated [21] Peptidase 18) Protein coding (1778 bp) Pathways: interferon gamma (ENSG00000184979) signaling and immune response IFN alpha/beta signaling pathway. Int. J. Mol. Sci. 2021, 22, 4456 20 of 46

Table 4. Cont.

Expression Gene Name Species Location Changes Function Refs. Ensembl ID Gene Type Length (PrRP vs. Control) Activities: aggregates to form Ifi44 microtubular structures. (Interferon Induced Mus musculus Chr 3 Diseases: Potocki-Shaffer Up-regulated [22] Protein 44) Protein coding (2860 bp) syndrome and hepatitis D. (ENSG00000137965) Pathways: interferon gamma signaling. Activities: enhances functional Rtp4 expression of the opioid receptor (Receptor Transporter Mus musculus Chr 16 heterodimer OPRM1-OPRD1. Up-regulated [23] Protein 4) Protein coding (1573 bp) Diseases: pain-related diseases. (ENSG00000136514) Pathways: olfactory transduction and signaling by GPCR.

Table 5. GO analysis of hub genes.

Gene GO Analysis [24,25] MF: RNA binding Ifit1 BP: immune system process; response to stimulus CC: cytosol MF: carbohydrate derivative binding; RNA binding; transferase Oasl2 BP: immune system process; response to stimulus CC: cytosol; nucleus; organelle lumen MF: DNA binding; transcription Irf7 BP: immune system process; nucleic acid-templated transcription; response to stimulus; signaling; system development CC: cytosol; nucleus; organelle lumen MF: RNA binding Ifit3 BP: immune system process; response to stimulus CC: cytosol; mitochondrion MF: RNA binding Ifit2 BP: cell death; immune system process; response to stimulus CC: cytosol; endoplasmic reticulum MF: hydrolase Usp18 BP: protein metabolic process; response to stimulus CC: cytosol; nucleus MF: none Ifi44 BP: immune system process; response to stimulus CC: none MF: signaling receptor binding Rtp4 BP: cellular component organization; establishment of localization; immune system process; response to stimulus CC: none Note: MF: Molecular Function; CC: Cellular Component; BP: Biological Process. This information was obtained from the Mouse Genome Database (MGD) at the Mouse Genome Informatics website, the Jackson Laboratory, Bar Harbor, Maine (URL: http://www.informatics.jax. org)[24,25] (date of retrieving data: 11 January 2021). Int. J. Mol. Sci. 2021, 22, 4456 21 of 46 Int. J. Mol. Sci. 2021, 22, x FOR PEER REVIEW 19 of 51

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Int. J. Mol. Sci. 2021, 22, x FOR PEER REVIEW 20 of 51

(D) Figure 7. Protein-protein interaction (PPI) analysis of DEGs using the Cytoscape Plug-in Mcode. The PPI interaction be- Figure 7. Protein-proteintween DEGs interaction was analyzed with (PPI) the Cytoscape analysis Plug-in of DEGs Mcode. using(A–D) Four the networks Cytoscape were activated Plug-in by the Mcode. up-regulated The PPI interaction between DEGs wasDEGs, analyzed and no network with thewas Cytoscapestimulated by the Plug-in down-regulated Mcode. DEGs. (A –D) Four networks were activated by the up-regulated DEGs, and no network was stimulated by the down-regulated DEGs.

2.5. Common Transcription Factors Tied to Genes Down-Regulated by PrRP To investigate the transcription factors of genes up-regulated by PrRP, TRRUST (ver- sion 2) was utilized. As demonstrated in Table6, sixteen transcription factors were collected (screening criterion: p < 0.05), which included Irf1, Stat1, Nfkb1, Jun, Irf8, Rel, Rela, Foxo3, Irf4, Ikbkb, Foxm1, Hdac1, Spi1, Cebpb, Fos, and Stat3. In addition, no transcription factors were obtained for the down-regulated DEGs.

Table 6. Transcriptional factors tied to the up-regulated DEGs.

# of Overlapped # Key TF Description p Value FDR Genes 1 Irf1 interferon regulatory factor 1 5 2.16 × 10−9 3.46 × 10−8 2 Stat1 signal transducer and activator of transcription 1 5 1.12 × 10−8 8.92 × 10−8 nuclear factor of kappa light polypeptide gene enhancer 3 Nfkb1 6 4.95 × 10−6 2.64 × 10−5 in B cells 1, p105 4 Jun jun proto-oncogene 5 1.02 × 10−5 4.07 × 10−5 5 Irf8 interferon regulatory factor 8 3 1.74 × 10−5 5.56 × 10−5 6 Rel reticuloendotheliosis oncogene 3 3.19 × 10−5 8.50 × 10−5 v-rel reticuloendotheliosis viral oncogene homolog A 7 Rela 4 1.47 × 10−4 0.000336 (avian) 8 Foxo3 forkhead box O3 2 1.71 × 10−4 0.000341 9 Irf4 interferon regulatory factor 4 2 2.60 × 10−4 0.000462 10 Ikbkb inhibitor of kappaB kinase beta 2 3.11 × 10−4 0.000498 11 Foxm1 forkhead box M1 2 1.18 × 10−3 0.00171 12 Hdac1 histone deacetylase 1 2 1.74 × 10−3 0.00232 spleen focus forming virus (SFFV) proviral integration 13 Spi1 2 3.54 × 10−3 0.00435 oncogene 14 Cebpb CCAAT/enhancer binding protein (C/EBP), beta 2 3.89 × 10−3 0.00445 15 Fos FBJ osteosarcoma oncogene 2 6.83 × 10−3 0.00729 16 Stat3 signal transducer and activator of transcription 3 2 1.32 × 10−2 0.0132 Note: data acquired from TRRUST (version 2) (https://www.grnpedia.org/trrust/, date of retrieving data: 9 January 2021). Int. J. Mol. Sci. 2021, 22, x FOR PEER REVIEW 24 of 51

Int. J. Mol. Sci. 2021, 22, 4456 2.6. Verification of Hub Genes with qPCR and Western Blot 23 of 46 In order to verify the accuracy of the RNA-seq data, qPCR was carried out. These hub genes were all protein-coding genes (IFIT1, OASL2, IRF7, 2.6.IFIT3, Verification IFIT2, of HubUSP18, Genes IFI44, with and qPCR RTP4 and Western). As displayed Blot in Figure 8A, PrRP (1 nM) up-regulatedIn order to verify the mRNAs the accuracy of eight of the hub RNA-seq genes data, and qPCRno hub was genes carried were out. down- These hubregulated, genes were which all protein-coding were consistent genes with (IFIT1, the OASL2, RNA-seq IRF7, results. IFIT3, Moreover, IFIT2, USP18, the IFI44, ex- andpressionRTP4). As of displayed the hub inprotein Figure 8wasA, PrRP also (1de nM)tected up-regulated by the Western the mRNAs blot. ofSince eight only hub genesa few and commercial no hub genes antibodies were down-regulated, were availabl whiche for were us, the consistent protein withexpression the RNA-seq data results.of two Moreover, hub proteins the expression (IFIT1 of and the hubUSP18) protein were was acquired. also detected As bydemonstrated the Western blot. in SinceFigure only a8B, few PrRP commercial showed antibodies no noticeable were availableeffect on forthe us, protein the protein expression expression of IFIT1 data of two hub proteins (IFIT1 and USP18) were acquired. As demonstrated in Figure8B, PrRP and USP18. showed no noticeable effect on the protein expression of IFIT1 and USP18.

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Figure 8. Cont. Int. J. Mol. Sci. 2021, 22, 4456 24 of 46 Int. J. Mol. Sci. 2021, 22, x FOR PEER REVIEW 25 of 51

Anti-USP18 Anti-IFIT1

Anti-Actin Anti-Actin

USP18 IFIT1 1.5 ns 2.0 ns

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0.0 0.0 Contro P Control Pr rRP RP 3 3 1 (1 nM) 1 l (1 n M)

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FigureFigure 8. Verification 8. Verification of hub of genes. hub genes. BMDMs BMDMs were incubatedwere incubated with PrRPwith PrRP (1nM) (1nM) for 18 for h, 18 followed h, followed by a by qPCR a qPCR detection and a Westerndetection blot. and ( Aa )Western Total RNA blot. was (A) extracted, Total RNA and was a extracted, qPCR detection and a wasqPCR carried detection out was to identify carried eight out to hub identify genes. The eight hub genes. The mRNA level was normalized by the expression of GAPDH. *, significantly different mRNA level was normalized by the expression of GAPDH. *, significantly different from the control group, ** p < 0.01; from the control group, ** p < 0.01; *** p < 0.001. Each detection was conducted three times in duplicate. The *** p < 0.001. Each detection was conducted three times in duplicate. The data were presented as the means ± S.E.M. data were presented as the means ± S.E.M. Statistical significance analysis was performed with the t-test Statisticalmethod. significance (B) BMDMs analysis were was lysed performed and subsequently with the t-test subjected method. to ( BWestern) BMDMs blot were examination lysed and subsequently subjected to Western(n = blot3). The examination data are shown (n = 3). as The the data means are shown± S.E.M. as *, the significantly means ± S.E.M. different *, significantly from the control different group; from ns, the no control group; ns,significance. no significance. Statistical Statistical significance significance analys analysisis was conducted was conducted with the with t-test the approach. t-test approach.

2.7. Protein Modeling of Hub Proteins 2.7. Protein Modeling of Hub Proteins To investigate the protein structure of hub proteins, the three-dimen- To investigate the protein structure of hub proteins, the three-dimensional structures sional structures of hub proteins were built using the Modeller (9v23). A total of hub proteins were built using the Modeller (9v23). A total of 1000 models for hub of 1000 models for hub proteins were acquired (except for IFI44 and RTP4), proteins were acquired (except for IFI44 and RTP4), and the model with the lowest DOPE and the model with the lowest DOPE value was collected (IFIT1: value was collected (IFIT1: −54845.33203; OASL2: −49878.21484; IRF7: −32177.46484; −54845.33203; OASL2: −49878.21484; IRF7: −32177.46484; IFIT3: −43405.51953; IFIT3: −43405.51953; IFIT2: −53080.90625; USP18: −41661.42188) (Figure9, Table7). IFIT2: −53080.90625; USP18: −41661.42188) (Figure 9, Table 7). Subsequently, these protein models were sent to the online MolProbity website to Subsequently, these protein models were sent to the online MolProbity evaluate the quality of these models. As displayed in Figure 10 and Table8, the ratio of residueswebsite located to inevaluate the outlier the quality region of of these proteins models. was rangedAs displayed from 0.01%in Figure to 0.04% 10 and (IFIT1: 0.004%;Table OASL2: 8, the 0.014%; ratio IRF7:of residues 0.042%; located IFIT3: 0.013%;in the outlier IFIT2: 0.019%;region of USP18: proteins 0.013%; was IFI44: 0.000%;ranged and RTP4: from 0.000%),0.01% to implying 0.04% (IFIT1: that the 0.004%; quality OASL2: of these 0.014%; models wasIRF7: acceptable. 0.042%; IFIT3: 0.013%; IFIT2: 0.019%; USP18: 0.013%; IFI44: 0.000%; and RTP4: 0.000%), implying that the quality of these models was acceptable.

Int. J. Mol. Sci. 2021, 22, 4456 25 of 46 Int. J. Mol. Sci. 2021, 22, x FOR PEER REVIEW 28 of 51

IFIT1 OASL2 IRF7 IFIT3

IFIT2 USP18 RTP4 IFI44

Figure 9. Superposition of the 3-D models of primarily modeled structure (gray) and MD-optimized protein structure (violet) (IFIT1, OASL2, IRF7, IFIT3, IFIT2, USP18, IFI44, and RTP4). The figure was producedFigure 9.withSuperposition the Pymol software of the(Delano, 3-D W.L. models The Pymol of primarily Molecular Graphics modeled System structure (2002) DeLano (gray) Scientific, and MD-optimized SanCarlos, CA, proteinUSA. http:// structurewww.pymol.org, date of retrieving data: 11(violet) November (IFIT1, 2020). OASL2, IRF7, IFIT3, IFIT2, USP18, IFI44, and RTP4). The figure was produced with the Pymol software (Delano, W.L. The Pymol Molecular Graphics System (2002) DeLano Scientific, SanCarlos, CA, USA. http://www.pymol.org, date of retrieving data: 11 November 2020).

Table 7. Protein modeling templates.

Protein Model Templates Proteins Species Length (aa) (Query Cover, Identify) 5W5H_A (2.79 Å) (99%, 52.98%) Ifit1 Mus musculus 461 6C6K_A (2.54 Å) (97%, 53.25%) 5UDI_A (1.58 Å) (95%, 53.22%) 4XQ7_A (1.60 Å) (68%, 49%) Oasl2 Mus musculus 439 4IG8_A (2.70 Å) (65%, 45.40%) 1PX5_A (1.74 Å) (66%, 43.02%) 1QWT_A (2.10 Å) (49%, 28.57%) Irf7 Mus musculus 330 1J2F_A (2.30 Å) (49%, 28.57%) 5JEJ_A (2.00 Å) (46%, 28.97%) 4G1T_A (2.80 Å) (99%, 51.33%) Ifit3 Mus musculus 391 6C6K_A (2.54 Å) (92%, 41.58%) 5W5H_A (2.79 Å) (92%, 41.58%) 4G1T_A (2.80 Å) (98%, 63.03%) Ifit2 Mus musculus 468 5UDI_A (1.58 Å) (92%, 41.03%) 5W5H_A (2.79 Å) (92%, 41.03%) 5CHV_A (3.00 Å) (87%, 100%) Usp18 Mus musculus 308 5CHT_A (2.80 Å) (87%, 100%) 2F1Z_A (3.20 Å) (87%, 26.89%) Ifi44 Mus musculus 420 De novo by Rosetta Rtp4 Mus musculus 247 De novo by Rosetta Int. J. Mol. Sci. 2021, 22, 4456 26 of 46 Int. J. Mol. Sci. 2021, 22, x FOR PEER REVIEW 29 of 51

IFIT1 OASL2

IRF7 IFIT3

IFIT2 USP18

Figure 10. Cont. Int. J. Mol. Sci. 2021, 22, 4456 27 of 46 Int. J. Mol. Sci. 2021, 22, x FOR PEER REVIEW 30 of 51

IFI44 RTP4

Figure 10.FigureRamachandran 10. Ramachandran analysis analysis of the homology of the homology modeled-models modeled-mode of hubls proteinsof hub proteins (IFIT1, (IFIT1, OASL2, OASL2, IRF7, IFIT3,IRF7, IFIT3, IFIT2, IFIT2, USP18, IFI44, and RTP4). The different colored areas: ‘disallowed’ (violet), ‘most favored’ (light blue), and USP18, IFI44, and RTP4). The different colored areas: ‘disallowed’ (violet), ‘most favored’ (light blue), and ‘generously ‘generously allowed’ (blue). allowed’ (blue).

Table 8. Ramachandran plot analysis.

Number of Residues Number of Residues Proteins Number of Outliers in Favoured Regions in Allowed Region Ifit1 452/461 (98%) 459/461 (99.6%) 2 (0.004%) Oasl2 408/439 (92.9%) 433/439 (98.6%) 6 (0.014%) Irf7 295/330 (89.4%) 316/330 (95.8%) 14 (0.042%) Ifit3 372/391 (95.1%) 386/391 (98.7%) 5 (0.013%) Ifit2 450/468 (96.2%) 459/468 (98.1%) 9 (0.019%) Usp18 292/308 (94.8%) 304/308 (98.7%) 4 (0.013%) Ifi44 399/420 (95.0%) 420/420 (100.0%) 0 (0.000%) Rtp4 237/247 (96.0%) 247/247 (100.0%) 0 (0.000%) Note: part of the N-terminal sequence of the hub proteins were removed for subsequent molecular dynamics simulation.

2.8. Molecular Dynamics Simulation of Hub Proteins Molecular dynamics (MD) simulation (at least 300 ns) was performed to study the behavior of the hub proteins (Figures9 and 11). The structural convergence data of hub proteins were displayed as RMSD (Figure 12), RMSF (Figure 13), and gyrate (Figure 14, Supplementary File 4: Table S3). To investigate the flexibility of hub proteins, the RMSF of the atoms of hub proteins was analyzed (Figure 13). Residues with high RMSF suggested high flexibility, whereas low RMSF values indicated few fluctuations between average positions and residues. As displayed in Table S3 and Figure 13, the average RMSF score varied from 0.2118 and 0.4669 (IFIT1: 0.2412; OASL2: 0.3507; IRF7: 0.3929; IFIT3: 0.4669; IFIT2: 0.2438; USP18: 0.2118; IFI44: 0.6008; and RTP4: 0.3364). Int. J. Mol. Sci. 2021, 22, 4456 28 of 46

Int. J. Mol. Sci. 2021, 22, x FOR PEER REVIEW 31 of 51

IFIT1 OASL2 IRF7 IFIT3

IFIT2 USP18 IFI44 RTP4 Figure 11. The three-dimensional structure of hub proteins. Three-dimensional models of MD-optimized hub proteins were shown, including IFIT1, OASL2, IRF7, IFIT3, IFIT2, USP18, IFI44,Figure and RTP4. 11. TheThe picture three-dimensional was generated with the structure Pymol software of hub (Delano, proteins. W.L. The Three-dimensional Pymol Molecular Graphics models System of(2002) MD-optimized DeLano Scientific, hubSanCarlos, proteins CA, USA. http://www.pymol.org,were shown, date including of retrieving IFIT1, data: 11 OASL2, November IRF7,2020). IFIT3, IFIT2, USP18, IFI44, and RTP4. The picture was generated with the

PymolInt. J. Mol. software Sci. 2021, 22, (Delano,x FOR PEER REVIEW W.L. The Pymol Molecular Graphics System (2002) DeLano Scientific,32 of 51 SanCarlos, CA, USA.

http://www.pymol.org, date of retrieving data: 11 November 2020).

(A)

(B) Figure 12. RMSD plots of hub proteins in molecular dynamics simulation (at least 300 ns) (back- Figure 12. RMSD plots ofbone hub Cα proteins atoms). To inshow molecular the deviations dynamics of DEGs protein simulation clearly, RMSD (at plots least were 300 presented. ns) (backbone The Cα atoms). To show the deviations of DEGs proteinRMSD of ( clearly,A) IFIT1, OASL2, RMSD IRF7, plots IFIT3 and were (B) IFIT2, presented. USP18, IFI44, The and RMSD RTP4 were of shown. (A) IFIT1, OASL2, IRF7, IFIT3 and (B) IFIT2, USP18, IFI44, and RTP4 were shown. Int. J. Mol. Sci. 2021, 22, x FOR PEER REVIEW 33 of 51 Int. J. Mol. Sci. 2021, 22, 4456 29 of 46

Figure 13. RMSF plots of hub proteins in molecular dynamics simulation (at least 300 ns). The RMSF of IFIT1, OASL2, IRF7, IFIT3,Figure IFIT2, 13. RMSF USP18, plots IFI44, of andhub RTP4proteins were in shown. molecular dynamics simulation (at least 300 ns). The RMSF of IFIT1, OASL2, IRF7, IFIT3, IFIT2, USP18, IFI44, and RTP4 were shown. Int. J. Mol. Sci. 2021, 22, x FOR PEER REVIEW 34 of 51 Int. J. Mol. Sci. 2021, 22, 4456 30 of 46

(A)

(B)

FigureFigure 14. 14.GyrationGyration radius radius plots plots of hub of hub proteins. proteins. The Thegyration gyration radius radius of (A of) IFIT1, (A) IFIT1, OASL2, OASL2, IRF7, IRF7, IFIT3 and (B) IFIT2, USP18, IFI44, and RTP4 were shown. IFIT3 and (B) IFIT2, USP18, IFI44, and RTP4 were shown.

2.9. Peptide-HubTo examine Protein the dynamicsDocking and structures of hub proteins, the RMSD of hub protein atomsTo explore was interpreted. the possible The interaction backbone between atom reached PrRP equilibriumand hub proteins, within the 2.21–4.68 Rosetta ns pro- from gramthe was initial employed stage (IFIT1: to analyze 2.61 ns; the OASL2: possible 2.55 dock ns; IRF7:binding 2.22 sites ns; for IFIT3: PrRP-hub 2.44 ns; proteins. IFIT2: 4.68 As ns; displayedUSP18: 4.02in Figure ns; IFI44: 15, PrRP 2.21 ns;was and not RTP4: wholly 2.93 embedded ns) (Figure in 12 the). Subsequently,protein structure. the structureAs ex- pected,started PrRP to converge bound to at the different N- or C-terminal time points region (IFIT1: of 3.2 the ns; hub OASL2: protein, 3.62 where ns; IRF7: located 2.85 at ns; theIFIT3: outside 5.12 of ns;the IFIT2:protein 4.95 structure. ns; USP18: 4.67 ns; IFI44: 2.62 ns; and RTP4: 3.58 ns). The structure of the hub proteins maintained a stable conformation till the end of simulation (IFIT1: 0.66–0.81; OASL2: 0.60–0.82; IRF7: 0.97–1.09; IFIT3: 0.93–1.78; IFIT2: 0.54–0.75; USP18: 0.31–0.22; IFI44: 0.92–1.42; and RTP4: 0.89–0.1.05). The radius of gyration (Rg) suggested the compression of the protein atoms, and a decrease in Rg indicated an unstable protein structure. As displayed in Figure 14, the average Rg score of hub protein varies from 1.943 to 2.836 (IFIT1: 2.738; OASL2: 2.449; IRF7: 2.235; IFIT3: 2.643; IFIT2: 2.705; USP18: 2.119; IFI44: 2.836; and RTP4: 1.943).

2.9. Peptide-Hub Protein Docking To explore the possible interaction between PrRP and hub proteins, the Rosetta pro- gram was employed to analyze the possible dock binding sites for PrRP-hub proteins. Int. J. Mol. Sci. 2021, 22, 4456 31 of 46

As displayed in Figure 15, PrRP was not wholly embedded in the protein structure. As Int. J. Mol. Sci. 2021, 22, x FOR PEER REVIEW expected, PrRP bound to the N- or C-terminal region of the hub protein, where located at 35 of 51 the outside of the protein structure.

IFIT1 OASL2 IRF7 IFIT3

IFIT2 USP18 IFI44 RTP4

Figure 15.Figure The docking 15. The dockinganalysis analysisof PrRP31 of and PrRP31 hub proteins. and hub proteins.There 3-D There structures 3-D structures of PrRP31-h ofub PrRP31-hub proteins were proteins demonstrated, were demon- including IFIT1, OASL2, IRF7, IFIT3, IFIT2, USP18, IFI44, and RTP4.strated, The picture including was IFIT1, drawn OASL2, with the IRF7, Pymo IFIT3,l software IFIT2, USP18, (Delano, IFI44, W.L. and The RTP4. Pymol The Molecul picturear was Graphics drawn withSystem the (2002) Pymol DeLano software Scientific, SanCarlos, CA, USA. http://www.pymol.org, date of retrieving(Delano, W.L. data: The 9 January Pymol Molecular2021). Graphics System (2002) DeLano Scientific, SanCarlos, CA, USA. http://www.pymol.org, date of retrieving data: 9 January 2021).

2.10. Expression of GPR10 on BMDMs The expression of GPR10 was examined in BMDMs by immunofluorescence stain and Western blot (Figure 16). As shown in Figure 16C, GPR10 protein was distributed on the cell membrane of BMDMs, and no signal was acquired with the IgG control (the negative control). PrRP 1 nM exposure (18 h) provoked a remarkable increase in the GPR10 protein (Figure 16A,B) than the control group. Int. J. Mol. Sci. 2021, 22, x FOR PEER REVIEW 36 of 51

2.10. Expression of GPR10 on BMDMs The expression of GPR10 was examined in BMDMs by immunofluorescence stain and Western blot (Figure 16). As shown in Figure 16C, GPR10 protein was distributed on Int. J. Mol. Sci. 2021, 22, 4456 32 of 46 the cell membrane of BMDMs, and no signal was acquired with the IgG control (the neg- ative control). PrRP 1 nM exposure (18 h) provoked a remarkable increase in the GPR10 protein (Figure 16A,B) than the control group.

(A)

Int. J. Mol. Sci. 2021, 22, x FOR PEER REVIEW 37 of 51

(B)

(C)

FigureFigure 16. The 16. expression The expression of GPR10 of GPR10 protein protein in BMDMsin BMDMs was was investigated investigated by by Western Western blot blot an andd immunofluorescence immunofluorescence stain- staining. ing. (A) BMDMs were incubated with PrRP31 (1 nM) for 18 h, followed by immunoblot detection for GPR10 protein level (A) BMDMs(n = 5). were (B) quantification incubated with of the PrRP31 bands in (1 (A). nM) The for data 18 h, were followed displayed by immunoblotas the means ± detectionS.E.M. *, significantly for GPR10 different protein levelfrom (n = 5). (B) quantificationthe control group; of the * bandsp < 0.05; in ** (A).p < 0.01; The *** data p < were0.001; displayed**** p < 0.0001. as theStatistical means significance± S.E.M. analysis *, significantly was conducted different using from the controlthe group; one-way * p peptide hormone secretion (Figure 4), suggesting that the expression profile of related genes was affected by PrRP. Given that macrophages are deeply involved in regulating multiple physiological pro- cesses of neuroendocrine [31], PrRP may affect the physiological activities of the neuroen- docrine field by regulating macrophages.

3.2. Common Transcription Factors Tied to PrRP-Regulated DEGs in BMDMs In order to capture the effects of DEGs on the transcription profile of macrophages at the transcriptome level, DEGs were subjected to TRRUST (version 2) for transcription fac- tor analysis. As demonstrated in Table 6, PrRP stimulated a series of general transcription factors, including Irf1 (interferon regulatory factor 1), Irf8 (interferon regulatory factor 8), Int. J. Mol. Sci. 2021, 22, 4456 33 of 46

3. Discussion 3.1. PrRP Modulated Different Functional Enrichment Pathways of BMDMs In 2012, the Romeroa team discoveries that PrRP promotes the production of pro- inflammatory cytokines (IL-8, IL-12, IL-1β, and IL-6) in Salmon salar, indicating that PrRP is a local transmitter of the innate immune pathway in leukocytes [13]. In the same line, in this study, PrRP significantly promoted inflammatory immune response pathways, including regulation of defense response, response to interferon-beta, response to interferon-gamma, inflammatory response (Figure4B,E,H). Overall, these data suggested that PrRP may be involved in inflammation and immune regulation processes. A series of reports show that PrRP plays a vital role in regulating food intake and energy metabolism [8,26–30]. In the present study, PrRP regulated several functional pathways responsible for macrophages, including neuron differentiation of the central nervous system, sodium ion transport, brain development, and peptide hormone secretion (Figure4), suggesting that the expression profile of related genes was affected by PrRP. Given that macrophages are deeply involved in regulating multiple physiological processes of neuroendocrine [31], PrRP may affect the physiological activities of the neuroendocrine field by regulating macrophages.

3.2. Common Transcription Factors Tied to PrRP-Regulated DEGs in BMDMs In order to capture the effects of DEGs on the transcription profile of macrophages at the transcriptome level, DEGs were subjected to TRRUST (version 2) for transcription factor analysis. As demonstrated in Table6, PrRP stimulated a series of general transcription factors, including Irf1 (interferon regulatory factor 1), Irf8 (interferon regulatory factor 8), and Irf4 (interferon regulatory factor 4). These data implied that PrRP might stimulate the related genes governed by these transcription factors. Besides, PrRP activated several differentiation-related transcription factors, including Stat1 (signal transducer and activator of transcription 1), Nfkb1 (nuclear factor of kappa light polypeptide gene enhancer in B cells 1, p105), Jun (jun proto-oncogene), Ikbkb (inhibitor of kappaB kinase beta), and Stat3 (signal transducer and activator of transcription 3), indicating that PrRP might be involved in the biological processes mastered by above transcription factors. Additionally, the number of down-regulated DEGs is too small to obtain transcription factors.

3.3. The Concentration of PrRP in the Experimental System for High-Throughput Sequencing In this study, the treatment concentration of PrRP on cells is a question worth of concern. To the best of our knowledge, the use of high-throughput sequencing (such as RNA-seq) to investigate the effects of PrRP on the transcriptome of target tissues or cells has not been reported yet. In our experimental system, PrRP 1 nM was selected as the concentration to treat cells based on the following considerations. On the one hand, the concentration of PrRP was limited to a range as close as possible to the physiological concentration in the body (around nM range), which would be helpful to simulate the real effect of neuropeptides in physiological conditions as much as possible. On the other hand, NPFF, another neuropeptide belonging to the RFamide peptide family as PrRP, provides us with a reference. Recently, Waqas and colleagues investigate the effects of NPFF on the gene expression of J774A.1 macrophages and 3T3-L1 preadipocytes by high throughput sequencing where cells were exposed to NPFF 1 nM for 18 h [15,32]. Moreover, our lab also examine the influence of NPFF on the mouse macrophages RAW 264.7 transcriptome where the same exposure (1 nM, 18 h) was applied to the cells [14]. Therefore, in this study, BMDMs were exposed to 1 nM PrRP for 18 h for subsequent RNA-seq detection.

3.4. Expression of PrRP-GPR10 on BMDMs PrRP-GPR10 is widely expressed in the neuroendocrine system. In the peripheral tissues, as a ligand in the PrRP-GPR10 system, PrRP mRNA is found in the lung, adrenal gland, liver, kidney, pancreas, gut, and reproductive organs [33,34]. In addition, PrRP is Int. J. Mol. Sci. 2021, 22, 4456 34 of 46

also distributed in the NTS, ventrolateral reticular, and DMN nucleus [6]. Meanwhile, as the receptor in the PrRP-GPR10 system, GPR10 mRNA is expressed in the rat adrenal medulla, epididymis, and testis [35]. Given that the pons and hypothalamus are both key parts of controlling food intake and energy metabolism [36], the above data shows that PrRP-GPR10 should play a key role in neuroendocrine metabolism and may even be a potential target for anti-obesity therapy [29,37,38]. In the central nervous system, GPR10 is expressed in the paraventricular hypothalamic (PVN), thalamic reticular (TRN), dorsomedial hypothalamic (DMN) nuclei, periventricular hypothalamic (PEVN), and the nucleus of the solitary tract (NTS) of the brainstem [39]. These data suggest that GPR10 is involved in energy metabolism and food intake [8,40]. Very recently, the Lenka Maletínská team find that GPR10 gene deletion in C57BL/6J mice causes significant metabolic disturbances, as GPR10 KO mice demonstrate enhanced basal neuronal activity, disturbed lipid homeostasis, and altered insulin sensitivity [37]. Moreover, the expression of PrRP-GPR10 in the immune system has already been reported. The Romeroa group demonstrates that PrRP mRNA is expressed in leukocytes from the head kidney and blood of Salmon salar. Moreover, synthetic PrRP provokes the production of pro-inflammatory cytokines (IL-8, IL-12, IL-1β, and IL-6), implying that PrRP could be a local transmitter of the innate immune pathway in leukocytes [13]. In the present study, GPR10 was found to be expressed on the cell membrane of BMDMs (Figure 16C), which increased after PrRP (1–1000 nM, 18 h) exposure (Figure 16A,B). These data indicated that PrRP might be involved in diverse physiological processes controlled by macrophages.

3.5. Prolactin and Inflammatory Processes To the best of our knowledge, there are few studies on the involvement of PrRP in the inflammatory process. However, prolactin, secreted by neurons stimulated by PrRP, is widely involved in inflammation and immune processes. Prolactin is a peptide hormone that is generated in the anterior pituitary gland and in various sites outside of the pituitary. It has been reported that prolactin is involved in a variety of biological processes, including lactation, reproduction, and immune functions. Elevated serum prolactin concentration is often associated with inflammation and immune response [41]. The Jörg-Matthias Brand team finds that prolactin stimulates a pro-inflammatory immune response on peripheral inflammatory cells [42]. Prolactin, at concentrations achiev- able during medication, pregnancy, and anesthesia, significantly enhances the synthesis of tumor necrosis factor-alpha (TNF-alpha) and interleukin (IL)-12 in lipopolysaccharide- activated human whole blood cultures. These data suggest that prolactin may affect pathophysiological processes in physiological hyperprolactinemic disorders [42]. Actually, prolactin has been considered an inflammatory cytokine, which inhibits the negative selection of autoreactive B lymphocytes [41]. Up to now, hyperprolactinemia has been found in patients with a variety of autoimmune diseases, including systemic lupus erythematosus, rheumatoid arthritis, multiple sclerosis, systemic sclerosis, and autoimmune thyroid disease [41,43–45]. These data indicate that prolactin is involved in the pathological process of the above diseases.

3.6. Prolactin and Macrophages Prolactin and its receptors are widely involved in the functional regulation of macrophages. On the one hand, prolactin promotes the release of cytokines, chemokines, and reactive oxygen species in macrophages [41,46–50]. On the other hand, the prolactin receptor is widely distributed through the immune system, such as macrophages, lymphocytes, mono- cytes, granulocytes, natural killer cells, and thymic epithelial cells [51]. Hence, prolactin and its receptor are widely involved in the immune response process. In the present study, PrRP differentially regulated the gene expression of mouse BMDMs. Besides, GPR10, the receptor of PrRP, was expressed on mouse BMDMs. Given Int. J. Mol. Sci. 2021, 22, 4456 35 of 46

that BMDMs are deeply involved in regulating the neuroendocrine system, our data suggested that PrRP may be involved in various physiological processes controlled by macrophages.

3.7. PrRP and Microglia Recently, the potential neuroprotective effect of PrRP has also been reported. By using a model of AD-like β-amyloid (Aβ) pathology (double transgenic APP/PS1 mice), Lenka Maletínskáa’s team find that a PrRP analog (palm11-PrRP31) reduces the total amount of senile Aβ plaques in APP/PS1 mice. Moreover, palm11-PrRP31 reduces the marker (ionized calcium-binding adaptor molecule 1 (Iba1)) of microglia near the Aβ plaque, suggesting that PrRP may have a potential neuroprotective effect [52]. Microglia are a type of cells derived from bone marrow hematopoietic stem cells. In the early stage of embryonic development, bone marrow hematopoietic stem cells enter the central nervous system and finally differentiate into microglia through the monocyte lineage [53]. As the resident macrophages in the central nervous system (CNS), microglia play an essential role in the innate and acquired immune response in local regions [54]. Given that microglia are mainly distributed in large non-overlapping areas throughout the CNS [55,56], and these areas may overlap with areas where PrRP is distributed (such as pons and hypothalamus) [33–35,40]. Therefore, a question arises: does PrRP regulate glial cells? Unfortunately, to the best of our knowledge, the research on the effect of PrRP on microglia has not been reported yet, which will be an exciting direction worth exploring.

3.8. Considerations of Double Positive Cells in the Control Group of Flow Cytometry Results In the present study, mouse BMDMs were differentiated from primary mouse bone marrow cells induced by cytokines. It is worth noting that the results of the control group (no antibody was added) showed that 0.14% of the cells were still positive for anti-F4/80 and anti-CD11b (Figure S1A). Regarding this phenomenon, we believe that the following reasons may be responsible for this.

3.8.1. Non-Specific Fluorescent Signal Caused by Dead Cells Primary cells are of great significance in biomedical research because they have certain advantages over cell lines in maintaining the physiological environment of biological samples. However, a significant shortcoming in primary cell research is the difficulty in obtaining a single type of cell with 100% purity. In our experimental system, the monocytes in the bone marrow of mice were isolated according to a widely used method [57–59], followed by cytokine induction and eventually formed BMDMs. It should be pointed out that this method has a certain probability of introducing non-specific cells that initially existed in the bone marrow of mice. These cells may die in an unsuitable environment and eventually introduce a non-specific fluorescent signal in the flow cytometry test results. Moreover, the fluorescent signal of dead cells is often displayed as a diagonal signal on the scatter plot of a flow cytometer (it should be noted that the fluorescent signal on the diagonal of the scatter plot does not mean that these signals are certainly come from dead cells, as these signals may be a superposition of signals from live and dead cells) [60–62]. Furthermore, the intensity of non-specific fluorescence produced by dead cells can be similar to the intensity of fluorescence produced by fluorescein, which may introduce non-specific fluorescence signals into the final flow cytometer results [60–62]. In this study, the fluorescence signal of the control group (no antibody was added) was on the diagonal line (Figure2B). Therefore, there is a possibility that the double-positive signal (only 0.14%) in the control cells may be part of the fluorescent signal introduced by dead cells.

3.8.2. Non-Specific Fluorescent Signal from Cell Debris or Tiny Tissue Pieces Cells or cell-like particulate matter is the object of flow cytometry analysis, and cell debris is inevitably present in the process of flow cytometry detection [62]. In this study, a series of measures were adopted to reduce non-specific signals detected by flow cytometry. Int. J. Mol. Sci. 2021, 22, 4456 36 of 46

Firstly, a widely used sterile filter (100 mesh) was used to obtain a single-cell suspension as much as possible during the experiment of obtaining mouse primary bone marrow cells. Secondly, in the flow cytometry detection step, a routinely used threshold was set to eliminate obvious cell debris and other non-target cells as much as possible. Even so, our experimental system was still unable to absolutely distinguish the tiny amounts of other components that may be introduced during the isolation and culture of primary cells from BMDMs (especially those with tiny tissue masses similar in size to BMDMs). These non-specific substances might cause minor non-specific signals in the final results. In our experimental system, the double-positive rate of BMDMs in the experimental group was 96.7%, which was significantly higher than the control group (no antibody was added, double-positive rate: 0.14%), indicating that the purity of BMDMs was acceptable.

3.8.3. The Excitation Wavelength of Fluorescein Is the Same as the Emission Wavelength, Which Is a Wavelength Range, Rather Than a Specific Value In this study, PE-conjugated anti-F4/80 and FITC-conjugated anti-CD11b were used. In addition, the detection results of the flow cytometer have also undergone channel com- pensation processing as usual [63–65]. However, the excitation wavelength of fluorescein (the same as the emission wavelength) belongs to a wavelength range rather than a specific value [64]. Hence, it is difficult to obtain a completely 100% pure signal. In our opinion, the above factors may also contribute to the small amount of double-positive fluorescence signal in the control group of the flow cytometry test result (Figure2B).

3.8.4. Non-Specific Fluorescence Signals Caused by BMDMs The intensity of the non-specific fluorescence of the cell is determined by the cell itself and is related to the size of the cell. Generally speaking, non-specific fluorescence produced by large cells is strong, while non-specific fluorescence produced by small cells is weak. Larger macrophages have stronger non-specific fluorescence than smaller lymphocytes [62,63,66]. In our study, flow cytometry was used to detect the purity of BMDMs, and the large volume of BMDMs might also introduce non-specific fluorescent signals for the control group (Figure2B).

3.9. The Limitations of Our Study Firstly, the changes in the protein levels of hub proteins are worth studying. In this study, through a series of bioinformatics methods, eight hub genes were obtained, which may be the critical nodes of PrRP regulating the gene network of BMDMs. Although the RNA-seq results of the hub genes were verified by qPCR, the changes in the protein levels of the eight hub genes would provide a solid biological basis for the significance of this study. However, limited by the fact that only a few commercial antibodies were currently available to us, the two hub proteins’ Western blot data (IFIT1 and USP18) were obtained. As shown in Figure8B, PrRP did not significantly change the protein levels of IFIT1 and USP18 (Figure8B). In our opinion, the following concerns would be helpful to understand this phenomenon. (1) Hub genes responded to PrRP stimulation in protein levels might be late than in mRNA level. After PrRP (1 nM) treatment for 18 h, the mRNA levels of IFIT1 and USP18 were significantly increased. However, the influence of PrRP on the protein expression of hub genes might be longer than 18 h. In the process of gene expression (from mRNA to protein), it takes minutes to hours to translate mRNA into proteins [67]. (2) PrRP may regulate the expression of the hub proteins of BMDMs in a variety of ways. Although PrRP (1 nM) treatment for 18 h significantly affected the expression of hub genes, this does not mean that PrRP will cause changes in hub proteins’ expression levels. In the step from mRNA to protein, a variety of post-transcriptional modifications can cause changes in protein levels, including mRNA degradation control, mRNA translocation control, protein degradation control, translation control, Int. J. Mol. Sci. 2021, 22, 4456 37 of 46

mRNA translocation control, and protein degradation control [67]. Therefore, these above links may be employed by PrRP to regulate the expression of the hub proteins of BMDMs. (3) PrRP may modulate the functions of the hub proteins of BMDMs in diverse manners. It is the function of proteins, rather than the protein expression, that plays a pivotal role in cellular activities [68]. Various biologically active molecules (including neu- ropeptides) may regulate each hub protein’s activity in a variety of ways, including phosphorylation, heterogeneous regulation, covalent modification, etc. [67]. How- ever, this study’s focus is to investigate the effect of PrRP on the transcriptomic gene expression of BMDMs, which may provide clues for the subsequent exploration of the functional regulation of PrRP on BMDMs. Therefore, follow-up studies on the complex biological functions of PrRP on BMDMs are worth looking in to. (4) The regulation of PrRP on the transcriptome gene expression of BMDMs may be more complicated than we previously assumed. Since proteins and various protein– protein interaction (PPI) networks are prominent members that play an essential role in regulating various biological processes in cells, the influence of neuropeptide PrRP on BMDMs may be a complicated process. In the same vein, emerging data have indicated that PrRP may have a wide range of effects in regulating the neuroendocrine system [40,69]. Secondly, the binding mode of PrRP and hub proteins needs to be verified by exper- iments. In this study, with the help of molecular simulation tools, the possible binding modes of PrRP and hub proteins were predicted, which may provide clues for exploring the mode of action of PrRP. However, reliable experimental verification is necessary to understand the mechanism of PrRP fully. Finally, the key “driver” of the network formed by PrRP-activated DEGs needs to be further studied. In the present study, our data described the gene-expression signature of murine bone marrow-derived macrophages from the transcriptome level, which may provide preliminary clues for the understanding of the possible effects of PrRP on the immune system. However, considering the complexity of the gene expression regulatory network [70], it is of great significance to identify the essential molecules of PrRP to regulate the gene expression of BMDMs from the experimental level-such as these “driver” genes.

4. Materials and Methods 4.1. Ethical Statement The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Ethics Committee of Northwestern Polytechnical University (protocol code 201900048, 2 January 2020).

4.2. Animals Male mice (C57BL/6 strain, 18–22 g) were kept in approved plastic cages with hu- midity of 65–74% and temperature of 20–22 ◦C and a photocycle of 12 h dark/light. The mice had free access to water and food. All measures were conducted to minimize animals suffering during the whole experiment process.

4.3. Reagents TRIzol, Dulbecco’s modified Eagle’s medium (DMEM), β-mercaptoethanol, and Fetal bovine serum (FBS) were purchased from Invitrogen™ and Gibco™ (Thermo Fisher Scientific, Inc., Waltham, MA, USA). Streptomycin (10,000 µg/mL)/penicillin (10,000 units/mL) antibiotics and Trypsin-EDTA solution (0.05% Trypsin—EDTA) were obtained from Merck-Millipore (Merck-Millipore, Ontario, Canada). Cell culture dishes were purchased from Corning, Inc. (Corning, NY, USA). Prime- Script TM 1st Strand cDNA Synthesis Kit and SYBR® Premix Ex Taq TM II kit were acquired from TaKaRa (Dalian, China). QiaQuick PCR extraction kit was obtained from Qiagen (Venlo, The Netherlands). The red blood cell lysing solution was from Beyotime (Shanghai, Int. J. Mol. Sci. 2021, 22, 4456 38 of 46

China). L-929 cells were purchased from the Stem Cell Bank of the Chinese Academy of Sciences (Shanghai, China). BCA Protein Assay Kit was from Thermo Scientific Pierce (Thermo Fisher scientific, MA, USA). PVDF membrane, ECL detection kit, and protease inhibitor cocktail III (EDTA- free) were provided by Millipore Corporation (Bedford, MA, USA). The Rabbit anti-GPR10 polyclonal antibody and horseradish peroxidase conjugated-goat anti-rabbit IgG was ac- quired from Thermo Fisher Scientific (Beverly, MA, USA). Rabbit anti-IFIT1 monoclonal antibody, rabbit anti-USP18 monoclonal antibody, rabbit anti-Actin monoclonal antibody, and anti-rabbit IgG (Alexa Fluor 488 Conjugate) were obtained from Cell Signaling Tech- nology (Beverly, MA, USA). FITC Rat anti-mouse CD11b antibody and PE Rat-anti mouse F4/80 antibody were from BD Pharmingen (San Diego, CA, USA). PrRP was synthesized by the GL Biochem Ltd. (Shanghai, China) using the solid- phase peptide synthesis method. The mass of the peptide was confirmed using a mass spectrometer (LCMS-2010EV, Shimadzu, Japan). PrRP31 was purified by HPLC, and peptide (purity > 98%) was used in this study.

4.4. BMDM Preparation BMDMs were isolated by flushing mouse tibias and femurs with cold PBS. Cells were centrifuged at 1200 rpm/min for 7 min (4 ◦C), and the pellet was resuspended with 3 mL red blood cell lysing solution for 5 min. Then, the cell suspensions were centrifuged at 800 rpm/min for 5 min (at 4 ◦C), and the pellet was resuspended with 10 mL PBS. Next, the cell suspensions were filtered through a sterile filter (100 mesh) to collect a single cell suspension. The single-cell suspensions were maintained in DMEM complete culture medium ◦ (streptomycin (100 µg/mL)/penicillin (100 units/mL), 10% FBS) at 37 C with 5% CO2 in a humidified incubator. Fourteen h later, the non-adherent cell supernatant was obtained, centrifuged at 800 rpm/min for 5 min (4 ◦C), and the pellet was resuspended in DMEM complete cell culture medium. Next, cells were maintained in a 100-mm culture dish supplemented with 20% L-929 conditioned media for 8 d (the fresh medium was refreshed every 3 d). At 8 days, the cells were differentiated into BMDMs, where more than 96% of the cells were double positive for F4/80 and CD11b.

4.5. RNA-seq Sample Preparation and Microscope Detection BMDMs were exposed to PrRP (1 nM) for 18 h, followed by RNA sequencing exam- ination. Cells were washed with PBS (3 times) and lysed with TRIzol (1 mL). Next, cell lysates were quickly stored in liquid nitrogen. Subsequently, the RNA-seq detection was performed by the Novogene Co Ltd. (Beijing, China). Cell images were acquired by a confocal microscope (Leica TCS SP5, Leica Microsys- tems, Mannheim, Germany). In addition, the detailed structure of BMDMs was detected by a transmission electron microscope HT7700 (Hitachi High-Technologies, Tokyo, Japan).

4.6. Flow Cytometry Detection Cells were rinsed twice with pre-cooled buffer (BSA-PBS-1%) and washed with trypsin (0.25%). The cells were resuspended in a fresh medium to form a uniform cell suspension, followed by centrifugation (1000/rpm, 5 min). Next, the pelleted cells acquired with centrifugation were resuspended in a buffer solution (BSA-PBS-1%) to form a uniform cell suspension. Five min later, the cells were centrifuged (1000/rpm, 5 min), and the pellet was resuspended in a buffer solution (BSA-PBS-1%). Then, the number of cells were counted (1 × 106/100 µL). The antibody was added into the cell suspension, followed by incubating in the dark for 25 min. During the whole incubation process, the cell mixture was mixed every 2 min to make sure the antibody and the cells to be thoroughly combined. Subsequently, the cell mixture was subjected to centrifugation (1000 rpm/min, 5 min), and the supernatant was discarded. Then, 400 uL of buffer solution (BSA-PBS-1%) was added into the tube to resuspend the cell pellet. Next, the whole-cell suspension was filtered with Int. J. Mol. Sci. 2021, 22, 4456 39 of 46

a 300-mesh sterile filter to ensure that only single cells were subjected to flow cytometry detection. Finally, the purity of macrophages was detected by flow cytometry (BD Calibur, Biosciences, San Jose, CA, USA), followed by analysis with the Cellquest (BD) and Modfit software.

4.7. RNA-seq Sample Collection and Preparation 4.7.1. RNA Quantification and Qualification BMDMs RNA was detected on 1% agarose gels. RNA purity was examined with a NanoPhotometer® spectrophotometer (IMPLEN, CA, USA). RNA integrity was investi- gated with a RNA Nano 6000 Assay Kit of the Bioanalyzer 2100 system (Agilent Tech- nologies, CA, USA). RNA concentration was assessed with a Qubit® RNA Assay Kit of Qubit®2.0 Fluorometer (Life Technologies, Carlsbad, CA, USA).

4.7.2. Library Construction for RNA Sequencing A total of 1 µg RNA (per sample) was acquired as input RNA for the RNA sample examination. RNA libraries were constructed with a NEBNext® UltraTM RNA Library Prep Kit for Illumina® (Lincoln, NE, USA).

4.7.3. Sequencing and Clustering The clustering of samples was conducted with the cBot Cluster Generation System and the TruSeq PE Cluster Kit v3-cBot-HS (Illumia). After cluster generation was carried out, the samples were sequenced with the Illumina Hiseq platform. Finally, 125 bp/150 bp paired-end reads were acquired.

4.8. RNA-seq Data Interpretation 4.8.1. Quality Control, Mapping Reads to the Reference Genome and Quantification of Gene Expression Low-quality reads were removed, and the clean reads with high quality were used. The paired-end clean reads were aligned to the musculus reference genome with the Hisat2 v2.0.5. Finally, the FPKM (fragments per kilobase of exon model per million reads mapped) of each gene was calculated, and the read was mapped to each gene by using the FeatureCounts v1.5.0-p3.

4.8.2. Differential Expression Interpretation Differential expression gene (DEGs) detection was conducted with the DESeq2 R package (1.16.1). The p-values were adjusted with the Benjamini and Hochberg’s method to analyze the false discovery rate (FDR). Genes with an adjusted p-value < 0.05 were considered as DEGs. The heat map of DEGs was generated by the toolkit TBtools [71].

4.8.3. GO and KEGG Enrichment Interpretation In order to analyze the ontology (GO) enrichment of differentially expressed genes (DEGs), the R package “ClusterProfiler” [72] was utilized. GO terms (adjusted p < 0.05) were considered as significantly enriched. To explore the pathways associated with DEGs, the enrichment study was performed with Metascape online tool (http://metascape.org/, date of retrieving data: 8 November 2020) [73,74], and the significant biological processes were obtained. To further investigate the functions of DEGs, DEGs were subjected to further analysis using PANTHER (http://www.pantherdb.org/, date of retrieving data: 11 November 2020) [75]. The main GO categories were acquired, including molecular function (MF), biological process (BP), and cellular component (CC). Besides, KEGG (http://www.genome.jp/kegg/, date of retrieving data: 11 November 2020) was used to explore the functional enrichment of DEGs. To show the KEGG pathway maps clearly, a Cytoscape plug-in KEGGParser was employed to show biological networks. Int. J. Mol. Sci. 2021, 22, 4456 40 of 46

4.8.4. Protein-Protein Interaction (PPI) Network Analysis In order to explore the interaction among DEGs, all DEGs were analyzed with the online tool STRING (http://string-db.org, date of retrieving data: 15 November 2020) [76]. Only experimentally proved interactions (a combined score > 0.4) were considered sig- nificant. Next, the PPI network was built with the Cytoscape software (Ver3.8.0), and the Cytoscape plug-in (Molecular Complex Detection, MCODE) was utilized to analyze the modules of the PPI network. Subsequently, function enrichment analysis for DEGs of the modules was conducted (p < 0.05 was considered as statistically significant). To further explore the pathways DEGs, pathway enrichment analysis was carried out by Cytoscape software and two plug-ins (clueGO (http://apps.cytoscape.org/apps/ cluego)[77] and Cluepedia (http://apps.cytoscape.org/apps/cluepedia, date of retrieving data: 15 November 2020)) [78]. Subsequently, KEGG pathway enrichment analysis was conducted with ClueGO and CluePedia tool kits, and pathways with p < 0.05 and kappa coefficient > 0.4 were selected. In order to identify the top-ranked hub genes from the PPI network, CytoHubba (a Cytoscape plug-in) was used in the following analysis. A total of eight hub genes were obtained according to the scores of the following methods, including MCC, MNC, Radiality, DMNC, Degree, Betweenness, Closeness, BottleNeck, EcCentricity, EPC, Stress and Clustering Coefficient [79,80].

4.9. qPCR Analysis This approach has been described elsewhere [81]. Briefly, total RNA was extracted from cells with the TRIzol following the manufacturer’s instruction. Then, cDNA was transcribed from 1 µg of RNA with a PrimeScript TM 1st Strand cDNA Synthesis Kit. Next, the gene expression level was detected with the SYBR® Premix Ex Taq TM II system and the MX3000P Real-Time PCR System (Stratagene). Real-time PCR conditions were set as follows with minor modifications: 94 ◦C for 30 s, 95 ◦C for 5 s, 56 ◦C. Data were normalized by GAPDH data using the comparative 2−∆∆CT approach. Primers were shown in Table9. Data were acquired in duplicates three times.

Table 9. Primers for qPCR.

Genes Primers Sequences (50 to 30) Products (bp) Forward TGTGTCCGTCGTGGATCTGA Gapdh 150 Reverse TTGCTGTTGAAGTCGCAGGAG Forward TTGTTGTTGTTGTTGTTC Ifit1 127 Reverse GTGAGTATGTATCCTTGG Forward TGTTGGATGATGAGGAGTTG Oasl2 75 Reverse GTATGATGGTGTCGCAGTC Forward AATCTACACTGAGTTCTG Irf7 154 Reverse GACCAAGTTTCACAAATG Forward GTCCTTTGAACTCCTACTC Ifit3 80 Reverse GCTCTCCTTACTGATGAC Forward TATATGACACAGACAGAG Ifit2 163 Reverse TCTAACTTCTTCCTATCC Forward CTTAGGTGACAGAACTTG Usp18 94 Reverse AACAGGAAGAAGAACTATTAG Forward TAGTTCTGCTTGCTTCTC Ifi44 180 Reverse TCTGTGCCTTCTTCATTC Forward TCAGAAGTGCCAGAAGTG Rtp4 123 Reverse TTCCTGTGTCCATAGTATCTC Int. J. Mol. Sci. 2021, 22, 4456 41 of 46

4.10. Western Blot Detection This approach has been previously described [82]. In brief, cells were washed with phosphate-buffered saline3 times and lysed by lysis solution (150 mM NaCl, 5 mM EGTA, 1% Nonidet P-40, 50 mM Tris/HCl, 0.5% sodium deoxycholate, 1 unit protease inhibitor cocktail III (EDTA-free), 0.1% SDS, PH 7.4). Then, cell lysates were centrifuged (12,000× g for 12 min, 4 ◦C), and the protein concentration of the supernatants was evaluated by the BCA Protein Assay Kit. Samples for immunoblot (18–24 µg/lane) were analyzed with the Bio-Rad mini-gel system by 10% SDS-polyacrylamide gel electrophoresis. Next, the proteins were blotted onto PVDF membranes using the Bio-Rad wet blotter system. The membranes were blocked with the solution (5% non-fat milk in the tris-buffered saline containing 0.05% tween-20) for 1 h. Then, the membranes were rinsed three times with TBST and were treated at 4 ◦C with appropriate antibodies (anti-GPR10 was 1:1000, and anti-Rabbit antibody was 1:10,000, and anti-Actin was 1:5000) for 12 h. The membranes were rinsed with TBST 3 times, followed by treated with horseradish peroxidase-conjugated secondary antibodies at room temperature for 2 h. Subsequently, the PVDF membranes were detected using an ECL detection kit. The band intensity was analyzed by the ChemDocTM XRS (Bio-Rad, Hercules, CA, USA) and Image J [83].

4.11. Immunofluorescence Stain Immunofluorescence stain was conducted as previously described [84]. In brief, BMDMs were cultured on the glass slides, followed by fixing with 4% paraformaldehyde for 15 min. Then, cells were rinsed with PBS 3 times, exposed to 0.1% Triton X-100 (12 min), and treated with 5% normal rabbit serum (3 h). Next, BMDMs were incubated with rabbit anti-GPR10 polyclonal antibody (1:250) at 4 ◦C for 14 h, followed by incubation with anti-rabbit IgG (Alexa Fluor 488 Conjugate) for 1.5 h. Subsequently, cells were treated with DAPI for 5 min to stain the nucleus. All figures were captured with a confocal microscope (Leica TCS SP5, Leica Microsystems, Mannheim, Germany).

4.12. Homology Modeling of Proteins The three-dimensional (3-D) structures of proteins were built using homology model- ing (the modeling of Ifi44 and Rtp4 was conducted with Robetta de novo structure predic- tion program [85,86] due to the lack of sufficient templates). Briefly, protein sequences were acquired from the NCBI nucleotide database (https://www.ncbi.nlm.nih.gov/protein/, date of retrieving data: 27 September 2020), and the blast module was employed to align the hub protein sequence in the PDB database [87]. Protein templates were retrieved from the online RCSB PDB Protein Data Bank. Firstly, three templates (query cover > 45%) for each protein were selected for homology modeling. Next, 3-D models of the hub protein were constructed using Modeller (9v23) [88]. Modules for multiple template modeling were used, including Align2d, Salign, and Model. One thousand candidate three-dimensional models for each protein were built, and the model with the lowest discrete optimized protein energy (DOPE) value was finally selected. Finally, all models were subjected to the online tool MolProbity (http://molprobity.biochem.duke.edu/, date of retrieving data: 13 December 2020) for quality evaluation [89].

4.13. Molecular Dynamics (MD) Simulation The molecular dynamics simulation of the hub protein was performed by using the Gromacs 2018.12 [90]. All simulations were carried out in the CHARMM36 force field [91].

4.13.1. Molecular Dynamic Simulation: Protein in Water Molecular dynamics simulation was performed in a condition with minor modifica- tions. The protein model was solvated in an octahedron box with a TIP3P water model (1.0 nm). The simulated system was neutralized by adding Cl− or Na+ ions, and periodic boundary conditions (PBC) were used in all directions. Next, energy minimization for the protein model was conducted with the steepest descent (50,000 steps) with the max force Int. J. Mol. Sci. 2021, 22, 4456 42 of 46

(<100 KJ/mol). Subsequently, the whole simulation was performed under equilibration phases with NVT (100 ps, 298.15 K) and NPT (300 ps, 298.15 K, 1.0 bar), respectively. The whole simulation was conducted at least 300 ns for each protein.

4.13.2. Molecular Dynamic Simulation: Analysis The MD trajectory was analyzed using GROMACS utilities to obtain the RMSF (root mean square fluctuation), RMSD (root mean square deviation), and radius of gyration (Rg). The 3-D structures of proteins were drawn with the Pymol software (Delano, W.L. The Pymol Molecular Graphics System (2002) DeLano Scientific, SanCarlos, CA, USA. http://www.pymol.org, date of retrieving data: 11 November 2020).

4.14. Dock The 3-D model of PrRP was constructed by PEP-FOLD3 [92], and the 3-D structure of hub proteins was obtained from the MD-optimized protein structures. (1) The primary docking complex, consisting of PrRP (ligand) and hub protein (receptor), was constructed by ZDOCK (3.02) [93–95]. (2) The primary docking complexes were sent to the Flexible peptide docking module of the Rosetta program (3.9) for subsequent docking. (A) Pre-pack mode: 1 model was constructed. (B) Low-resolution ab-initio analysis: 100 models were obtained, and 1 model with the best docking score was chosen for subsequent analysis. (C) Refinement analysis: 100 docking models were acquired, and a docking model was finally selected based on the total_score.

4.15. Statistical Analysis Data were demonstrated as means ± standard error of the mean (S.E.M). Data were interpreted by one-ANOVA followed by the post hoc tests (Tukey). The statistical analysis was performed by using the GraphPad Prism software version 8.0 (San Diego, CA, USA).

5. Conclusions Our work showed that PrRP significantly changed the transcriptome profile of BMDMs, implying that PrRP may be involved in various physiological activities mastered by macrophages. Our data provided clues for in-depth exploration of the role of PrRP in regulating diverse physiological processes governed by macrophages.

Supplementary Materials: The following are available online at https://www.mdpi.com/article/10 .3390/ijms22094456/s1. Author Contributions: Conceptualization, Y.S.; methodology, Y.S., Z.Z. and Y.K.; software, Y.S., Z.Z. and Y.K.; validation, Y.S., Z.Z. and Y.K.; investigation, Y.S., Z.Z. and Y.K.; resources, Y.S.; data curation, Y.S., Z.Z. and Y.K.; writing—original draft preparation, Y.S.; writing—review and editing, Y.S.; visualization, Y.S.; supervision, Y.S.; project administration, Y.S.; funding acquisition, Y.S. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by the Natural Science Basic Research Plan in Shaanxi Province of China (No. 2020JM-148), the Shaanxi Province Postdoctoral Science Foundation (Grant No. 2018BSHYDZZ48), and the China Postdoctoral Science Foundation (No. 2017M623250). Institutional Review Board Statement: The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Ethics Committee of Northwestern Polytechnical University (protocol code 201900048, 2 January 2020). Informed Consent Statement: Not applicable. Data Availability Statement: The data presented in this study are available in article and Supple- mentary Material. Acknowledgments: The authors thank Jia Bin, Wang Zhe, Zhouqi Yang, Technician Ren Leiqi, Technician Zhang Weiju, and Technician Deng Xiaoni for the experiments. We also appreciate the Int. J. Mol. Sci. 2021, 22, 4456 43 of 46

kind support from the Central Laboratory of School of Life Sciences, Northwestern Polytechnical University. The authors are sincerely grateful to the two anonymous reviewers for their detailed and very professional advice, which helped us significantly improve the manuscript’s quality. Conflicts of Interest: The authors declare no conflict of interest.

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